Prudential Life Insurance Data Analysis
Reading Prudential Life Insurance Data
Quick Snapshot of Data followed by seperating individual variable types
names(trainingData) [1] "Id" "Product_Info_1" "Product_Info_2" "Product_Info_3"
[5] "Product_Info_4" "Product_Info_5" "Product_Info_6" "Product_Info_7"
[9] "Ins_Age" "Ht" "Wt" "BMI"
[13] "Employment_Info_1" "Employment_Info_2" "Employment_Info_3" "Employment_Info_4"
[17] "Employment_Info_5" "Employment_Info_6" "InsuredInfo_1" "InsuredInfo_2"
[21] "InsuredInfo_3" "InsuredInfo_4" "InsuredInfo_5" "InsuredInfo_6"
[25] "InsuredInfo_7" "Insurance_History_1" "Insurance_History_2" "Insurance_History_3"
[29] "Insurance_History_4" "Insurance_History_5" "Insurance_History_7" "Insurance_History_8"
[33] "Insurance_History_9" "Family_Hist_1" "Family_Hist_2" "Family_Hist_3"
[37] "Family_Hist_4" "Family_Hist_5" "Medical_History_1" "Medical_History_2"
[41] "Medical_History_3" "Medical_History_4" "Medical_History_5" "Medical_History_6"
[45] "Medical_History_7" "Medical_History_8" "Medical_History_9" "Medical_History_10"
[49] "Medical_History_11" "Medical_History_12" "Medical_History_13" "Medical_History_14"
[53] "Medical_History_15" "Medical_History_16" "Medical_History_17" "Medical_History_18"
[57] "Medical_History_19" "Medical_History_20" "Medical_History_21" "Medical_History_22"
[61] "Medical_History_23" "Medical_History_24" "Medical_History_25" "Medical_History_26"
[65] "Medical_History_27" "Medical_History_28" "Medical_History_29" "Medical_History_30"
[69] "Medical_History_31" "Medical_History_32" "Medical_History_33" "Medical_History_34"
[73] "Medical_History_35" "Medical_History_36" "Medical_History_37" "Medical_History_38"
[77] "Medical_History_39" "Medical_History_40" "Medical_History_41" "Medical_Keyword_1"
[81] "Medical_Keyword_2" "Medical_Keyword_3" "Medical_Keyword_4" "Medical_Keyword_5"
[85] "Medical_Keyword_6" "Medical_Keyword_7" "Medical_Keyword_8" "Medical_Keyword_9"
[89] "Medical_Keyword_10" "Medical_Keyword_11" "Medical_Keyword_12" "Medical_Keyword_13"
[93] "Medical_Keyword_14" "Medical_Keyword_15" "Medical_Keyword_16" "Medical_Keyword_17"
[97] "Medical_Keyword_18" "Medical_Keyword_19" "Medical_Keyword_20" "Medical_Keyword_21"
[101] "Medical_Keyword_22" "Medical_Keyword_23" "Medical_Keyword_24" "Medical_Keyword_25"
[105] "Medical_Keyword_26" "Medical_Keyword_27" "Medical_Keyword_28" "Medical_Keyword_29"
[109] "Medical_Keyword_30" "Medical_Keyword_31" "Medical_Keyword_32" "Medical_Keyword_33"
[113] "Medical_Keyword_34" "Medical_Keyword_35" "Medical_Keyword_36" "Medical_Keyword_37"
[117] "Medical_Keyword_38" "Medical_Keyword_39" "Medical_Keyword_40" "Medical_Keyword_41"
[121] "Medical_Keyword_42" "Medical_Keyword_43" "Medical_Keyword_44" "Medical_Keyword_45"
[125] "Medical_Keyword_46" "Medical_Keyword_47" "Medical_Keyword_48" "Response" head(trainingData)head(testData)categoricalVarNames = c(paste('Product_Info_', c(1:3,5:7), sep=''),
paste('Employment_Info_', c(2,3,5), sep=""),
paste('InsuredInfo_', 1:7, sep=''),
paste('Insurance_History_', c(1:4,7:9), sep=''),
'Family_Hist_1',
paste('Medical_History_', c(2:14, 16:23, 25:31, 33:41), sep=''))
continuousVarNames = c('Product_Info_4',
'Ins_Age',
'Ht',
'Wt',
'BMI',
'Employment_Info_1',
'Employment_Info_4',
'Employment_Info_6',
'Insurance_History_5',
'Family_Hist_2',
'Family_Hist_3',
'Family_Hist_4',
'Family_Hist_5')
discreteVarNames = c('Medical_History_1',
'Medical_History_15',
'Medical_History_24',
'Medical_History_32',
paste('Medical_Keyword_', 1:48, sep=''))
train_categorical = trainingData %>% select(categoricalVarNames)
test_categorical = testData %>% select(categoricalVarNames)
train_continuous = trainingData %>% select(continuousVarNames)
test_continuous = testData %>% select(continuousVarNames)
train_discrete = trainingData %>% select(discreteVarNames)
test_discrete = testData %>% select(discreteVarNames)Let’s take a look at the categorical variable type
skim(train_categorical)Skim summary statistics
n obs: 59381
n variables: 61
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
Product_Info_2 0 59381 59381 2 2 0 19
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Employment_Info_2 0 59381 59381 8.64 4.23 1 9 9 9 38 <U+2582><U+2587><U+2583><U+2581><U+2581><U+2581><U+2581><U+2581>
Employment_Info_3 0 59381 59381 1.3 0.72 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Employment_Info_5 0 59381 59381 2.14 0.35 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Family_Hist_1 0 59381 59381 2.69 0.48 1 2 3 3 3 <U+2581><U+2581><U+2581><U+2583><U+2581><U+2581><U+2581><U+2587>
Insurance_History_1 0 59381 59381 1.73 0.45 1 1 2 2 2 <U+2583><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Insurance_History_2 0 59381 59381 1.06 0.33 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Insurance_History_3 0 59381 59381 2.15 0.99 1 1 3 3 3 <U+2586><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Insurance_History_4 0 59381 59381 1.96 0.95 1 1 2 3 3 <U+2587><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2587>
Insurance_History_7 0 59381 59381 1.9 0.97 1 1 1 3 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2586>
Insurance_History_8 0 59381 59381 2.05 0.76 1 1 2 3 3 <U+2585><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2586>
Insurance_History_9 0 59381 59381 2.42 0.51 1 2 2 3 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2586>
InsuredInfo_1 0 59381 59381 1.21 0.42 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2581>
InsuredInfo_2 0 59381 59381 2.01 0.086 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
InsuredInfo_3 0 59381 59381 5.84 2.67 1 3 6 8 11 <U+2582><U+2587><U+2581><U+2586><U+2581><U+2587><U+2581><U+2582>
InsuredInfo_4 0 59381 59381 2.88 0.32 2 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
InsuredInfo_5 0 59381 59381 1.03 0.23 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
InsuredInfo_6 0 59381 59381 1.41 0.49 1 1 1 2 2 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2586>
InsuredInfo_7 0 59381 59381 1.04 0.27 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_10 58824 557 59381 141.12 107.76 0 8 229 240 240 <U+2585><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_11 0 59381 59381 2.99 0.095 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_12 0 59381 59381 2.06 0.23 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_13 0 59381 59381 2.77 0.64 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_14 0 59381 59381 2.97 0.2 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_16 0 59381 59381 1.33 0.74 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Medical_History_17 0 59381 59381 2.98 0.15 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_18 0 59381 59381 1.05 0.23 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_19 0 59381 59381 1.03 0.18 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_2 0 59381 59381 253.99 178.62 1 112 162 418 648 <U+2582><U+2587><U+2581><U+2581><U+2582><U+2581><U+2582><U+2581>
Medical_History_20 0 59381 59381 1.99 0.12 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_21 0 59381 59381 1.11 0.31 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_22 0 59381 59381 1.98 0.13 1 2 2 2 2 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_23 0 59381 59381 2.53 0.85 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_25 0 59381 59381 1.19 0.41 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2581>
Medical_History_26 0 59381 59381 2.81 0.39 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2587>
Medical_History_27 0 59381 59381 2.98 0.2 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_28 0 59381 59381 1.07 0.25 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_29 0 59381 59381 2.54 0.84 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_3 0 59381 59381 2.1 0.3 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_30 0 59381 59381 2.04 0.2 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_31 0 59381 59381 2.99 0.17 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_33 0 59381 59381 2.8 0.59 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_34 0 59381 59381 2.69 0.72 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_35 0 59381 59381 1 0.064 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_36 0 59381 59381 2.18 0.41 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2582>
Medical_History_37 0 59381 59381 1.94 0.24 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_38 0 59381 59381 1 0.069 1 1 1 1 2 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_39 0 59381 59381 2.83 0.56 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_4 0 59381 59381 1.65 0.48 1 1 2 2 2 <U+2585><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_40 0 59381 59381 2.97 0.25 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_41 0 59381 59381 1.64 0.93 1 1 1 3 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2583>
Medical_History_5 0 59381 59381 1.01 0.086 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_6 0 59381 59381 2.89 0.46 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_7 0 59381 59381 2.01 0.17 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_8 0 59381 59381 2.04 0.29 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_9 0 59381 59381 1.77 0.42 1 2 2 2 3 <U+2582><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Product_Info_1 0 59381 59381 1.03 0.16 1 1 1 1 2 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Product_Info_3 0 59381 59381 24.42 5.07 1 26 26 26 38 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2587><U+2581><U+2581>
Product_Info_5 0 59381 59381 2.01 0.083 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Product_Info_6 0 59381 59381 2.67 0.74 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Product_Info_7 0 59381 59381 1.04 0.29 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>skim(test_categorical)Skim summary statistics
n obs: 19765
n variables: 61
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
Product_Info_2 0 19765 19765 2 2 0 19
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Employment_Info_2 0 19765 19765 8.44 4.19 1 9 9 9 37 <U+2582><U+2587><U+2583><U+2581><U+2581><U+2581><U+2581><U+2581>
Employment_Info_3 0 19765 19765 1.33 0.74 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Employment_Info_5 0 19765 19765 2.15 0.36 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Family_Hist_1 0 19765 19765 2.69 0.48 1 2 3 3 3 <U+2581><U+2581><U+2581><U+2583><U+2581><U+2581><U+2581><U+2587>
Insurance_History_1 0 19765 19765 1.71 0.46 1 1 2 2 2 <U+2583><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Insurance_History_2 0 19765 19765 1.05 0.31 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Insurance_History_3 0 19765 19765 2.18 0.98 1 1 3 3 3 <U+2586><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Insurance_History_4 0 19765 19765 1.92 0.95 1 1 2 3 3 <U+2587><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2586>
Insurance_History_7 0 19765 19765 1.89 0.96 1 1 1 3 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2586>
Insurance_History_8 0 19765 19765 2.04 0.77 1 1 2 3 3 <U+2586><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2586>
Insurance_History_9 0 19765 19765 2.4 0.51 1 2 2 3 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2586>
InsuredInfo_1 0 19765 19765 1.2 0.41 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2581>
InsuredInfo_2 0 19765 19765 2.01 0.086 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
InsuredInfo_3 0 19765 19765 5.7 2.98 1 3 8 8 11 <U+2583><U+2585><U+2581><U+2581><U+2581><U+2587><U+2581><U+2581>
InsuredInfo_4 0 19765 19765 2.89 0.31 2 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
InsuredInfo_5 0 19765 19765 1.03 0.24 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
InsuredInfo_6 0 19765 19765 1.42 0.49 1 1 1 2 2 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2586>
InsuredInfo_7 0 19765 19765 1.03 0.26 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_10 19564 201 19765 149.82 102.92 0 22 223 240 240 <U+2585><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_11 0 19765 19765 2.99 0.092 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_12 0 19765 19765 2.05 0.23 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_13 0 19765 19765 2.77 0.64 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_14 0 19765 19765 2.97 0.19 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_16 0 19765 19765 1.32 0.74 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Medical_History_17 0 19765 19765 2.98 0.15 2 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_18 0 19765 19765 1.05 0.23 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_19 0 19765 19765 1.03 0.18 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_2 0 19765 19765 249.79 177.94 1 112 162 407 647 <U+2582><U+2587><U+2581><U+2581><U+2582><U+2581><U+2582><U+2581>
Medical_History_20 0 19765 19765 1.99 0.12 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_21 0 19765 19765 1.11 0.31 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_22 0 19765 19765 1.98 0.12 1 2 2 2 2 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_23 0 19765 19765 2.49 0.87 1 1 3 3 3 <U+2583><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_25 0 19765 19765 1.2 0.41 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2581>
Medical_History_26 0 19765 19765 2.81 0.4 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2582><U+2581><U+2581><U+2581><U+2587>
Medical_History_27 0 19765 19765 2.98 0.19 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_28 0 19765 19765 1.07 0.25 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_29 0 19765 19765 2.56 0.83 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_3 0 19765 19765 2.15 0.35 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Medical_History_30 0 19765 19765 2.04 0.2 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_31 0 19765 19765 2.99 0.17 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_33 0 19765 19765 2.8 0.6 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_34 0 19765 19765 2.7 0.71 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_35 0 19765 19765 1 0.069 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_36 0 19765 19765 2.18 0.41 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2582>
Medical_History_37 0 19765 19765 1.94 0.25 1 2 2 2 2 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_38 0 19765 19765 1.01 0.072 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_39 0 19765 19765 2.83 0.56 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_4 0 19765 19765 1.66 0.47 1 1 2 2 2 <U+2585><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_40 0 19765 19765 2.97 0.25 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_41 0 19765 19765 1.65 0.94 1 1 1 3 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2583>
Medical_History_5 0 19765 19765 1.01 0.086 1 1 1 1 2 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_6 0 19765 19765 2.89 0.46 1 3 3 3 3 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Medical_History_7 0 19765 19765 2.01 0.17 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_8 0 19765 19765 2.04 0.29 1 2 2 2 3 <U+2581><U+2581><U+2581><U+2587><U+2581><U+2581><U+2581><U+2581>
Medical_History_9 0 19765 19765 1.78 0.42 1 2 2 2 2 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Product_Info_1 0 19765 19765 1.02 0.16 1 1 1 1 2 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Product_Info_3 0 19765 19765 24.32 5.18 2 26 26 26 37 <U+2581><U+2581><U+2581><U+2581><U+2581><U+2587><U+2581><U+2581>
Product_Info_5 0 19765 19765 2.01 0.081 2 2 2 2 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Product_Info_6 0 19765 19765 2.66 0.75 1 3 3 3 3 <U+2582><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2587>
Product_Info_7 0 19765 19765 1.04 0.29 1 1 1 1 3 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>Let’s explore continuous variables
skim(train_continuous)Skim summary statistics
n obs: 59381
n variables: 13
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100
BMI 0 59381 59381 0.47 0.12 0 0.39 0.45 0.53 1
Employment_Info_1 19 59362 59381 0.078 0.082 0 0.035 0.06 0.1 1
Employment_Info_4 6779 52602 59381 0.0063 0.033 0 0 0 0 1
Employment_Info_6 10854 48527 59381 0.36 0.35 0 0.06 0.25 0.55 1
Family_Hist_2 28656 30725 59381 0.47 0.15 0 0.36 0.46 0.58 1
Family_Hist_3 34241 25140 59381 0.5 0.14 0 0.4 0.52 0.6 1
Family_Hist_4 19184 40197 59381 0.44 0.16 0 0.32 0.42 0.56 0.94
Family_Hist_5 41811 17570 59381 0.48 0.13 0 0.4 0.51 0.58 1
Ht 0 59381 59381 0.71 0.074 0 0.65 0.71 0.76 1
Ins_Age 0 59381 59381 0.41 0.2 0 0.24 0.4 0.57 1
Insurance_History_5 25396 33985 59381 0.0017 0.0073 0 4e-04 0.00097 0.002 1
Product_Info_4 0 59381 59381 0.33 0.28 0 0.077 0.23 0.49 1
Wt 0 59381 59381 0.29 0.089 0 0.23 0.29 0.35 1
hist
<U+2581><U+2581><U+2583><U+2587><U+2585><U+2582><U+2581><U+2581>
<U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
<U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
<U+2587><U+2583><U+2582><U+2583><U+2581><U+2581><U+2581><U+2583>
<U+2581><U+2582><U+2585><U+2587><U+2586><U+2583><U+2581><U+2581>
<U+2581><U+2581><U+2583><U+2586><U+2587><U+2585><U+2581><U+2581>
<U+2581><U+2582><U+2587><U+2587><U+2586><U+2585><U+2582><U+2581>
<U+2581><U+2581><U+2583><U+2586><U+2587><U+2582><U+2581><U+2581>
<U+2581><U+2581><U+2581><U+2581><U+2582><U+2587><U+2585><U+2581>
<U+2583><U+2587><U+2587><U+2587><U+2587><U+2586><U+2581><U+2581>
<U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
<U+2587><U+2587><U+2582><U+2585><U+2581><U+2581><U+2581><U+2582>
<U+2581><U+2586><U+2587><U+2582><U+2581><U+2581><U+2581><U+2581>skim(test_continuous)Skim summary statistics
n obs: 19765
n variables: 13
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100
BMI 0 19765 19765 0.47 0.12 0.097 0.38 0.45 0.53 1
Employment_Info_1 3 19762 19765 0.079 0.084 0 0.035 0.06 0.1 1
Employment_Info_4 2137 17628 19765 0.0065 0.035 0 0 0 0 1
Employment_Info_6 3787 15978 19765 0.37 0.35 0 0.055 0.25 0.6 1
Family_Hist_2 9880 9885 19765 0.47 0.16 0.043 0.36 0.46 0.58 0.94
Family_Hist_3 11064 8701 19765 0.5 0.14 0 0.41 0.52 0.61 0.89
Family_Hist_4 6677 13088 19765 0.45 0.16 0 0.32 0.44 0.56 1
Family_Hist_5 13624 6141 19765 0.49 0.13 0.027 0.42 0.52 0.59 0.85
Ht 0 19765 19765 0.71 0.074 0.35 0.65 0.71 0.76 1
Ins_Age 0 19765 19765 0.41 0.2 0 0.25 0.42 0.58 0.97
Insurance_History_5 8105 11660 19765 0.0016 0.0026 0 0.00037 0.00083 0.002 0.13
Product_Info_4 0 19765 19765 0.32 0.28 0 0.077 0.23 0.49 1
Wt 0 19765 19765 0.29 0.09 0.084 0.23 0.29 0.35 0.88
hist
<U+2581><U+2582><U+2587><U+2587><U+2583><U+2581><U+2581><U+2581>
<U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
<U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
<U+2587><U+2583><U+2582><U+2583><U+2581><U+2581><U+2581><U+2583>
<U+2581><U+2582><U+2586><U+2587><U+2585><U+2583><U+2582><U+2581>
<U+2581><U+2581><U+2582><U+2585><U+2587><U+2587><U+2582><U+2581>
<U+2581><U+2583><U+2587><U+2587><U+2586><U+2583><U+2581><U+2581>
<U+2581><U+2581><U+2582><U+2585><U+2586><U+2587><U+2582><U+2581>
<U+2581><U+2581><U+2581><U+2585><U+2587><U+2585><U+2581><U+2581>
<U+2583><U+2587><U+2587><U+2587><U+2587><U+2587><U+2582><U+2581>
<U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
<U+2587><U+2587><U+2582><U+2585><U+2581><U+2581><U+2581><U+2582>
<U+2582><U+2587><U+2587><U+2582><U+2581><U+2581><U+2581><U+2581>Let’s explore discrete variables
skim(train_discrete)Skim summary statistics
n obs: 59381
n variables: 52
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Medical_History_1 8889 50492 59381 7.96 13.03 0 2 4 9 240 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_15 44596 14785 59381 123.76 98.52 0 17 117 240 240 <U+2587><U+2582><U+2582><U+2582><U+2581><U+2581><U+2581><U+2587>
Medical_History_24 55580 3801 59381 50.64 78.15 0 1 8 64 240 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_32 58274 1107 59381 11.97 38.72 0 0 0 2 240 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_1 0 59381 59381 0.042 0.2 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_10 0 59381 59381 0.036 0.19 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_11 0 59381 59381 0.058 0.23 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_12 0 59381 59381 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_13 0 59381 59381 0.006 0.077 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_14 0 59381 59381 0.0078 0.088 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_15 0 59381 59381 0.19 0.39 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Medical_Keyword_16 0 59381 59381 0.013 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_17 0 59381 59381 0.0092 0.095 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_18 0 59381 59381 0.0075 0.086 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_19 0 59381 59381 0.0093 0.096 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_2 0 59381 59381 0.0089 0.094 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_20 0 59381 59381 0.0081 0.09 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_21 0 59381 59381 0.015 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_22 0 59381 59381 0.037 0.19 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_23 0 59381 59381 0.098 0.3 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_24 0 59381 59381 0.019 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_25 0 59381 59381 0.089 0.29 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_26 0 59381 59381 0.013 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_27 0 59381 59381 0.012 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_28 0 59381 59381 0.015 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_29 0 59381 59381 0.012 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_3 0 59381 59381 0.049 0.22 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_30 0 59381 59381 0.025 0.16 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_31 0 59381 59381 0.011 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_32 0 59381 59381 0.021 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_33 0 59381 59381 0.023 0.15 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_34 0 59381 59381 0.021 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_35 0 59381 59381 0.0069 0.083 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_36 0 59381 59381 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_37 0 59381 59381 0.067 0.25 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_38 0 59381 59381 0.0068 0.082 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_39 0 59381 59381 0.014 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_4 0 59381 59381 0.015 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_40 0 59381 59381 0.057 0.23 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_41 0 59381 59381 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_42 0 59381 59381 0.046 0.21 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_43 0 59381 59381 0.011 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_44 0 59381 59381 0.0075 0.086 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_45 0 59381 59381 0.014 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_46 0 59381 59381 0.0085 0.092 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_47 0 59381 59381 0.02 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_48 0 59381 59381 0.054 0.23 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_5 0 59381 59381 0.0086 0.092 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_6 0 59381 59381 0.013 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_7 0 59381 59381 0.014 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_8 0 59381 59381 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_9 0 59381 59381 0.0067 0.081 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>skim(test_discrete)Skim summary statistics
n obs: 19765
n variables: 52
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Medical_History_1 2972 16793 19765 7.83 12.56 0 2 4 9 235 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_15 14864 4901 19765 125.69 99.12 0 19 119 240 240 <U+2586><U+2582><U+2582><U+2582><U+2581><U+2581><U+2581><U+2587>
Medical_History_24 18585 1180 19765 49.85 77.21 0 1 9 60.25 240 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_History_32 19414 351 19765 10.94 36.81 0 0 0 1.5 240 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_1 0 19765 19765 0.043 0.2 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_10 0 19765 19765 0.037 0.19 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_11 0 19765 19765 0.061 0.24 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_12 0 19765 19765 0.0077 0.088 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_13 0 19765 19765 0.0079 0.089 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_14 0 19765 19765 0.0085 0.092 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_15 0 19765 19765 0.2 0.4 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2582>
Medical_Keyword_16 0 19765 19765 0.011 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_17 0 19765 19765 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_18 0 19765 19765 0.007 0.083 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_19 0 19765 19765 0.0086 0.092 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_2 0 19765 19765 0.0077 0.087 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_20 0 19765 19765 0.0075 0.086 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_21 0 19765 19765 0.015 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_22 0 19765 19765 0.039 0.19 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_23 0 19765 19765 0.1 0.3 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_24 0 19765 19765 0.019 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_25 0 19765 19765 0.1 0.31 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_26 0 19765 19765 0.013 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_27 0 19765 19765 0.011 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_28 0 19765 19765 0.015 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_29 0 19765 19765 0.011 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_3 0 19765 19765 0.053 0.22 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_30 0 19765 19765 0.024 0.15 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_31 0 19765 19765 0.011 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_32 0 19765 19765 0.02 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_33 0 19765 19765 0.024 0.15 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_34 0 19765 19765 0.019 0.14 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_35 0 19765 19765 0.0067 0.081 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_36 0 19765 19765 0.011 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_37 0 19765 19765 0.066 0.25 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_38 0 19765 19765 0.007 0.083 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_39 0 19765 19765 0.014 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_4 0 19765 19765 0.014 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_40 0 19765 19765 0.057 0.23 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_41 0 19765 19765 0.011 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_42 0 19765 19765 0.045 0.21 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_43 0 19765 19765 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_44 0 19765 19765 0.0082 0.09 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_45 0 19765 19765 0.014 0.12 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_46 0 19765 19765 0.0086 0.092 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_47 0 19765 19765 0.018 0.13 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_48 0 19765 19765 0.056 0.23 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_5 0 19765 19765 0.0079 0.089 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_6 0 19765 19765 0.013 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_7 0 19765 19765 0.012 0.11 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_8 0 19765 19765 0.01 0.1 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>
Medical_Keyword_9 0 19765 19765 0.0076 0.087 0 0 0 0 1 <U+2587><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581><U+2581>From the above skim statements, we see some missing data. What is the precentage of missing data in training and test datasets
sum(is.na(trainingData)) / (nrow(trainingData) * ncol(trainingData))[1] 0.05171885sum(is.na(testData)) / (nrow(testData) * ncol(testData))[1] 0.05205894apply(trainingData, 2, function(x){
sum(is.na(x))
}) Id Product_Info_1 Product_Info_2 Product_Info_3
0 0 0 0
Product_Info_4 Product_Info_5 Product_Info_6 Product_Info_7
0 0 0 0
Ins_Age Ht Wt BMI
0 0 0 0
Employment_Info_1 Employment_Info_2 Employment_Info_3 Employment_Info_4
19 0 0 6779
Employment_Info_5 Employment_Info_6 InsuredInfo_1 InsuredInfo_2
0 10854 0 0
InsuredInfo_3 InsuredInfo_4 InsuredInfo_5 InsuredInfo_6
0 0 0 0
InsuredInfo_7 Insurance_History_1 Insurance_History_2 Insurance_History_3
0 0 0 0
Insurance_History_4 Insurance_History_5 Insurance_History_7 Insurance_History_8
0 25396 0 0
Insurance_History_9 Family_Hist_1 Family_Hist_2 Family_Hist_3
0 0 28656 34241
Family_Hist_4 Family_Hist_5 Medical_History_1 Medical_History_2
19184 41811 8889 0
Medical_History_3 Medical_History_4 Medical_History_5 Medical_History_6
0 0 0 0
Medical_History_7 Medical_History_8 Medical_History_9 Medical_History_10
0 0 0 58824
Medical_History_11 Medical_History_12 Medical_History_13 Medical_History_14
0 0 0 0
Medical_History_15 Medical_History_16 Medical_History_17 Medical_History_18
44596 0 0 0
Medical_History_19 Medical_History_20 Medical_History_21 Medical_History_22
0 0 0 0
Medical_History_23 Medical_History_24 Medical_History_25 Medical_History_26
0 55580 0 0
Medical_History_27 Medical_History_28 Medical_History_29 Medical_History_30
0 0 0 0
Medical_History_31 Medical_History_32 Medical_History_33 Medical_History_34
0 58274 0 0
Medical_History_35 Medical_History_36 Medical_History_37 Medical_History_38
0 0 0 0
Medical_History_39 Medical_History_40 Medical_History_41 Medical_Keyword_1
0 0 0 0
Medical_Keyword_2 Medical_Keyword_3 Medical_Keyword_4 Medical_Keyword_5
0 0 0 0
Medical_Keyword_6 Medical_Keyword_7 Medical_Keyword_8 Medical_Keyword_9
0 0 0 0
Medical_Keyword_10 Medical_Keyword_11 Medical_Keyword_12 Medical_Keyword_13
0 0 0 0
Medical_Keyword_14 Medical_Keyword_15 Medical_Keyword_16 Medical_Keyword_17
0 0 0 0
Medical_Keyword_18 Medical_Keyword_19 Medical_Keyword_20 Medical_Keyword_21
0 0 0 0
Medical_Keyword_22 Medical_Keyword_23 Medical_Keyword_24 Medical_Keyword_25
0 0 0 0
Medical_Keyword_26 Medical_Keyword_27 Medical_Keyword_28 Medical_Keyword_29
0 0 0 0
Medical_Keyword_30 Medical_Keyword_31 Medical_Keyword_32 Medical_Keyword_33
0 0 0 0
Medical_Keyword_34 Medical_Keyword_35 Medical_Keyword_36 Medical_Keyword_37
0 0 0 0
Medical_Keyword_38 Medical_Keyword_39 Medical_Keyword_40 Medical_Keyword_41
0 0 0 0
Medical_Keyword_42 Medical_Keyword_43 Medical_Keyword_44 Medical_Keyword_45
0 0 0 0
Medical_Keyword_46 Medical_Keyword_47 Medical_Keyword_48 Response
0 0 0 0 apply(testData, 2, function(x){
sum(is.na(x))
}) Id Product_Info_1 Product_Info_2 Product_Info_3
0 0 0 0
Product_Info_4 Product_Info_5 Product_Info_6 Product_Info_7
0 0 0 0
Ins_Age Ht Wt BMI
0 0 0 0
Employment_Info_1 Employment_Info_2 Employment_Info_3 Employment_Info_4
3 0 0 2137
Employment_Info_5 Employment_Info_6 InsuredInfo_1 InsuredInfo_2
0 3787 0 0
InsuredInfo_3 InsuredInfo_4 InsuredInfo_5 InsuredInfo_6
0 0 0 0
InsuredInfo_7 Insurance_History_1 Insurance_History_2 Insurance_History_3
0 0 0 0
Insurance_History_4 Insurance_History_5 Insurance_History_7 Insurance_History_8
0 8105 0 0
Insurance_History_9 Family_Hist_1 Family_Hist_2 Family_Hist_3
0 0 9880 11064
Family_Hist_4 Family_Hist_5 Medical_History_1 Medical_History_2
6677 13624 2972 0
Medical_History_3 Medical_History_4 Medical_History_5 Medical_History_6
0 0 0 0
Medical_History_7 Medical_History_8 Medical_History_9 Medical_History_10
0 0 0 19564
Medical_History_11 Medical_History_12 Medical_History_13 Medical_History_14
0 0 0 0
Medical_History_15 Medical_History_16 Medical_History_17 Medical_History_18
14864 0 0 0
Medical_History_19 Medical_History_20 Medical_History_21 Medical_History_22
0 0 0 0
Medical_History_23 Medical_History_24 Medical_History_25 Medical_History_26
0 18585 0 0
Medical_History_27 Medical_History_28 Medical_History_29 Medical_History_30
0 0 0 0
Medical_History_31 Medical_History_32 Medical_History_33 Medical_History_34
0 19414 0 0
Medical_History_35 Medical_History_36 Medical_History_37 Medical_History_38
0 0 0 0
Medical_History_39 Medical_History_40 Medical_History_41 Medical_Keyword_1
0 0 0 0
Medical_Keyword_2 Medical_Keyword_3 Medical_Keyword_4 Medical_Keyword_5
0 0 0 0
Medical_Keyword_6 Medical_Keyword_7 Medical_Keyword_8 Medical_Keyword_9
0 0 0 0
Medical_Keyword_10 Medical_Keyword_11 Medical_Keyword_12 Medical_Keyword_13
0 0 0 0
Medical_Keyword_14 Medical_Keyword_15 Medical_Keyword_16 Medical_Keyword_17
0 0 0 0
Medical_Keyword_18 Medical_Keyword_19 Medical_Keyword_20 Medical_Keyword_21
0 0 0 0
Medical_Keyword_22 Medical_Keyword_23 Medical_Keyword_24 Medical_Keyword_25
0 0 0 0
Medical_Keyword_26 Medical_Keyword_27 Medical_Keyword_28 Medical_Keyword_29
0 0 0 0
Medical_Keyword_30 Medical_Keyword_31 Medical_Keyword_32 Medical_Keyword_33
0 0 0 0
Medical_Keyword_34 Medical_Keyword_35 Medical_Keyword_36 Medical_Keyword_37
0 0 0 0
Medical_Keyword_38 Medical_Keyword_39 Medical_Keyword_40 Medical_Keyword_41
0 0 0 0
Medical_Keyword_42 Medical_Keyword_43 Medical_Keyword_44 Medical_Keyword_45
0 0 0 0
Medical_Keyword_46 Medical_Keyword_47 Medical_Keyword_48
0 0 0 Now let’s look at the response variable. The response variable is of nominal type and has levels from 1 to 8.
temp1 = ggplot(trainingData, aes(x = Response)) + geom_histogram(fill = 'Blue', alpha=0.5)
ggplotly(temp1, color = ~Response, width = 400, height = 200) %>% layout(title= 'Distribution of Response Variable', plot_bgcolor= 'white', xaxis = list(gridcolor = 'lightgrey', opacity = 0.2), yaxis = list(gridcolor = 'lightgrey', opacity = 0.2), autosize = F, width = 400, height = 200)`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()Based on the above plot we see that Response = 8 is most common and Response = 3 is least common in the dataset. Now let’s look at how missing values relate to these responses
noofNAperResponseType <- sapply(sort(unique(trainingData$Response)), function(x) {
apply(trainingData[trainingData$Response == x, ], 2, function(y) {
sum(is.na(y))
})
})
noofNAperResponseType [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
Id 0 0 0 0 0 0 0 0
Product_Info_1 0 0 0 0 0 0 0 0
Product_Info_2 0 0 0 0 0 0 0 0
Product_Info_3 0 0 0 0 0 0 0 0
Product_Info_4 0 0 0 0 0 0 0 0
Product_Info_5 0 0 0 0 0 0 0 0
Product_Info_6 0 0 0 0 0 0 0 0
Product_Info_7 0 0 0 0 0 0 0 0
Ins_Age 0 0 0 0 0 0 0 0
Ht 0 0 0 0 0 0 0 0
Wt 0 0 0 0 0 0 0 0
BMI 0 0 0 0 0 0 0 0
Employment_Info_1 16 0 0 0 0 3 0 0
Employment_Info_2 0 0 0 0 0 0 0 0
Employment_Info_3 0 0 0 0 0 0 0 0
Employment_Info_4 789 679 85 148 560 1752 883 1883
Employment_Info_5 0 0 0 0 0 0 0 0
Employment_Info_6 1171 1194 220 333 1221 1688 1365 3662
InsuredInfo_1 0 0 0 0 0 0 0 0
InsuredInfo_2 0 0 0 0 0 0 0 0
InsuredInfo_3 0 0 0 0 0 0 0 0
InsuredInfo_4 0 0 0 0 0 0 0 0
InsuredInfo_5 0 0 0 0 0 0 0 0
InsuredInfo_6 0 0 0 0 0 0 0 0
InsuredInfo_7 0 0 0 0 0 0 0 0
Insurance_History_1 0 0 0 0 0 0 0 0
Insurance_History_2 0 0 0 0 0 0 0 0
Insurance_History_3 0 0 0 0 0 0 0 0
Insurance_History_4 0 0 0 0 0 0 0 0
Insurance_History_5 2848 2710 565 725 2372 4369 2901 8906
Insurance_History_7 0 0 0 0 0 0 0 0
Insurance_History_8 0 0 0 0 0 0 0 0
Insurance_History_9 0 0 0 0 0 0 0 0
Family_Hist_1 0 0 0 0 0 0 0 0
Family_Hist_2 4049 3891 479 551 2840 5628 4354 6864
Family_Hist_3 2709 3101 611 971 3060 6170 4073 13546
Family_Hist_4 3099 2744 303 355 1929 3679 2994 4081
Family_Hist_5 3373 4017 745 1119 3701 7835 5221 15800
Medical_History_1 694 694 231 311 796 1744 998 3421
Medical_History_2 0 0 0 0 0 0 0 0
Medical_History_3 0 0 0 0 0 0 0 0
Medical_History_4 0 0 0 0 0 0 0 0
Medical_History_5 0 0 0 0 0 0 0 0
Medical_History_6 0 0 0 0 0 0 0 0
Medical_History_7 0 0 0 0 0 0 0 0
Medical_History_8 0 0 0 0 0 0 0 0
Medical_History_9 0 0 0 0 0 0 0 0
Medical_History_10 6106 6468 1003 1419 5379 11042 7980 19427
Medical_History_11 0 0 0 0 0 0 0 0
Medical_History_12 0 0 0 0 0 0 0 0
Medical_History_13 0 0 0 0 0 0 0 0
Medical_History_14 0 0 0 0 0 0 0 0
Medical_History_15 3981 4518 111 154 4241 8147 6428 17016
Medical_History_16 0 0 0 0 0 0 0 0
Medical_History_17 0 0 0 0 0 0 0 0
Medical_History_18 0 0 0 0 0 0 0 0
Medical_History_19 0 0 0 0 0 0 0 0
Medical_History_20 0 0 0 0 0 0 0 0
Medical_History_21 0 0 0 0 0 0 0 0
Medical_History_22 0 0 0 0 0 0 0 0
Medical_History_23 0 0 0 0 0 0 0 0
Medical_History_24 5755 6018 928 1327 5078 10047 7580 18847
Medical_History_25 0 0 0 0 0 0 0 0
Medical_History_26 0 0 0 0 0 0 0 0
Medical_History_27 0 0 0 0 0 0 0 0
Medical_History_28 0 0 0 0 0 0 0 0
Medical_History_29 0 0 0 0 0 0 0 0
Medical_History_30 0 0 0 0 0 0 0 0
Medical_History_31 0 0 0 0 0 0 0 0
Medical_History_32 6059 6413 957 1352 5338 10690 7999 19466
Medical_History_33 0 0 0 0 0 0 0 0
Medical_History_34 0 0 0 0 0 0 0 0
Medical_History_35 0 0 0 0 0 0 0 0
Medical_History_36 0 0 0 0 0 0 0 0
Medical_History_37 0 0 0 0 0 0 0 0
Medical_History_38 0 0 0 0 0 0 0 0
Medical_History_39 0 0 0 0 0 0 0 0
Medical_History_40 0 0 0 0 0 0 0 0
Medical_History_41 0 0 0 0 0 0 0 0
Medical_Keyword_1 0 0 0 0 0 0 0 0
Medical_Keyword_2 0 0 0 0 0 0 0 0
Medical_Keyword_3 0 0 0 0 0 0 0 0
Medical_Keyword_4 0 0 0 0 0 0 0 0
Medical_Keyword_5 0 0 0 0 0 0 0 0
Medical_Keyword_6 0 0 0 0 0 0 0 0
Medical_Keyword_7 0 0 0 0 0 0 0 0
Medical_Keyword_8 0 0 0 0 0 0 0 0
Medical_Keyword_9 0 0 0 0 0 0 0 0
Medical_Keyword_10 0 0 0 0 0 0 0 0
Medical_Keyword_11 0 0 0 0 0 0 0 0
Medical_Keyword_12 0 0 0 0 0 0 0 0
Medical_Keyword_13 0 0 0 0 0 0 0 0
Medical_Keyword_14 0 0 0 0 0 0 0 0
Medical_Keyword_15 0 0 0 0 0 0 0 0
Medical_Keyword_16 0 0 0 0 0 0 0 0
Medical_Keyword_17 0 0 0 0 0 0 0 0
Medical_Keyword_18 0 0 0 0 0 0 0 0
Medical_Keyword_19 0 0 0 0 0 0 0 0
Medical_Keyword_20 0 0 0 0 0 0 0 0
Medical_Keyword_21 0 0 0 0 0 0 0 0
Medical_Keyword_22 0 0 0 0 0 0 0 0
Medical_Keyword_23 0 0 0 0 0 0 0 0
Medical_Keyword_24 0 0 0 0 0 0 0 0
Medical_Keyword_25 0 0 0 0 0 0 0 0
Medical_Keyword_26 0 0 0 0 0 0 0 0
Medical_Keyword_27 0 0 0 0 0 0 0 0
Medical_Keyword_28 0 0 0 0 0 0 0 0
Medical_Keyword_29 0 0 0 0 0 0 0 0
Medical_Keyword_30 0 0 0 0 0 0 0 0
Medical_Keyword_31 0 0 0 0 0 0 0 0
Medical_Keyword_32 0 0 0 0 0 0 0 0
Medical_Keyword_33 0 0 0 0 0 0 0 0
Medical_Keyword_34 0 0 0 0 0 0 0 0
Medical_Keyword_35 0 0 0 0 0 0 0 0
Medical_Keyword_36 0 0 0 0 0 0 0 0
Medical_Keyword_37 0 0 0 0 0 0 0 0
Medical_Keyword_38 0 0 0 0 0 0 0 0
Medical_Keyword_39 0 0 0 0 0 0 0 0
Medical_Keyword_40 0 0 0 0 0 0 0 0
Medical_Keyword_41 0 0 0 0 0 0 0 0
Medical_Keyword_42 0 0 0 0 0 0 0 0
Medical_Keyword_43 0 0 0 0 0 0 0 0
Medical_Keyword_44 0 0 0 0 0 0 0 0
Medical_Keyword_45 0 0 0 0 0 0 0 0
Medical_Keyword_46 0 0 0 0 0 0 0 0
[ reached getOption("max.print") -- omitted 3 rows ]round(colSums(noofNAperResponseType) / sum(noofNAperResponseType), digits=2)[1] 0.10 0.11 0.02 0.02 0.09 0.19 0.13 0.34From this we see that data with Response as 8 has most missing values whereas Response equals 3 has least missing values. Now let’s look for Duplicated and Constant Rows
cat('Number of duplicated rows in Training dataset = ', nrow(trainingData) - nrow(unique(trainingData)), '\n')Number of duplicated rows in Training dataset = 0 cat('Number of constant rows in Training dataset = ', sum(sapply(trainingData, function(x){ length(unique(x)) == 1})), '\n')Number of constant rows in Training dataset = 0 #How to tell a dplyr function to apply it columnwise
cat('Number of duplicated rows in Test dataset = ', nrow(testData) - nrow(unique(testData)), '\n')Number of duplicated rows in Test dataset = 0 cat('Number of constant rows in Test dataset = ', sum(sapply(testData, function(x){ length(unique(x)) == 1})), '\n')Number of constant rows in Test dataset = 0 Now that we have looked at missing values, checked for duplication and constant rows. Let’s look at individual data types and visualize the distribution.
Plot densities of continuous features
tempDataset = train_continuous %>% select(c(1, 2)) %>% gather()
tempdenPlot = ggplot(data = tempDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots')
ggplotly(tempdenPlot, height= 600, width = 800) %>%
layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()
physicalAttDataset = train_continuous %>% select(Ht, Wt, BMI) %>% gather()
tempdenPlot = ggplot(data = physicalAttDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Physical Characteristics')
ggplotly(tempdenPlot, height= 600, width = 800) %>%
layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()
employAttDataset = train_continuous %>% select(Employment_Info_1, Employment_Info_6) %>% gather()
tempdenPlot = ggplot(data = employAttDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Employment Info 1 and 6')
ggplotly(tempdenPlot, height= 600, width = 800) %>%
layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)Removed 10873 rows containing non-finite values (stat_density).Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()
employDataset = train_continuous %>% select(Employment_Info_4) %>% gather()
tempdenPlot = ggplot(data = employDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Employment Info 4')
ggplotly(tempdenPlot, height= 600, width = 800) %>%
layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)Removed 6779 rows containing non-finite values (stat_density).Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()
familyHistDataset = train_continuous %>% select(starts_with('Family_Hist_')) %>% gather()
tempdenPlot = ggplot(data = familyHistDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Family History')
ggplotly(tempdenPlot, height= 600, width = 800) %>%
layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)Removed 123892 rows containing non-finite values (stat_density).Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()
insurHistDataset = train_continuous %>% select(c(9)) %>% gather()
tempdenPlot = ggplot(data = insurHistDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots')
ggplotly(tempdenPlot, height= 800, width = 1000) %>%
layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 800)Removed 25396 rows containing non-finite values (stat_density).Specifying width/height in layout() is now deprecated.
Please specify in ggplotly() or plot_ly()Plot Boxplots of Continuous Variables
tempDataset = train_continuous %>% select(c(1, 2, 9)) %>% gather()
plot_ly(data = tempDataset, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots')Ignoring 25396 observationsIgnoring 25396 observations
plot_ly(data = physicalAttDataset, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots of Physical Characteristics')
plot_ly(data = familyHistDataset, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots of Family History Attributes')Ignoring 123892 observationsIgnoring 123892 observations
employInfoData = train_continuous %>% select(starts_with('Employment_Info_')) %>% gather()
plot_ly(data = employInfoData, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots of Employment Information Attributes')Ignoring 17652 observationsIgnoring 17652 observationsHistograms of Categorical Variables
subSelectionVals = c('Product_Info', 'Employment_Info', 'InsuredInfo', 'Insurance_History', 'Family_Hist', 'Medical_History')
for(i in subSelectionVals) {
tempCatDataset = train_categorical %>% select(starts_with(i))
tempcolNames = names(tempCatDataset)
for(j in tempcolNames){
if(j == 'Product_Info_2' | j == 'Medical_History_10') next
tempCatPlot = ggplot(data = train_categorical) + geom_histogram(mapping = aes_string(x = j), fill = 'Blue', alpha = 0.5)
tempCatPlot = ggplotly(tempCatPlot, color = ~j, width = 400, height = 200) %>% layout(title= 'Distribution of Categorical Variable', plot_bgcolor= 'white', xaxis = list(gridcolor = 'lightgrey', opacity = 0.2), yaxis = list(gridcolor = 'lightgrey', opacity = 0.2), autosize = F, width = 400, height = 200)
print(tempCatPlot)
}
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Please specify in ggplotly() or plot_ly()From the density plots it looks like Employment_Info_6 has a very similar histogram to the response variable. Its a good indication that this variable maybe a good predictor of the response variable.
Now let’s explore to see the relationship and any collinearity that exist between the variables. First lets plot a correlation matrix of all continuous and categorical variables with response
corInputVarNames = c(categoricalVarNames, continuousVarNames)
corInputData = trainingData %>% select(corInputVarNames) %>% select(-Product_Info_2)
cor1 = cor(corInputData)
corDatalongform = melt(cor1)
ggplot(data = corDatalongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix excl. Discrete Variables (Medical Keywords)')
From this we see some interesting observations: i. Medica_History_25 and Medical_History_26 show negative correlation. ii. Variable 7 is strongly correlated with variables 18 and 14.
Now let’s look at detail by subsetting the data to identify correlation that exist between bins of input data ie types like Medical History, Product Info etc.
medHistData = trainingData %>% select(starts_with('Medical_History'), Response, -c(Medical_History_10, Medical_History_24, Medical_History_32))
cormedHist = cor(medHistData)
corMedHistlongform = melt(cormedHist)
ggplot(data = corMedHistlongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Medical History Variables')
From this graph we can see that: i. Medical_History_25 and Medical_History_26 show negative correlation ii. Medical_History_36 is postively correlated to Medical_History_15 and negatively correlated to Medical_History_26 iii. Medical_History_7 is storgly correlated with Medical_History_8 and Medical_History_14
physicalData = trainingData %>% select(Ins_Age, BMI, Ht, Wt, Response)
corPhyData = cor(physicalData)
corPhyDatalongForm = melt(corPhyData)
ggplot(data = corPhyDatalongForm, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Physical Attributes')
From this graph its interesting to see that Response is negatively correlated with Wt/BMI and Ins_Age.
insData = trainingData %>% select(starts_with('Insurance_History'), Response)
corinsData = cor(insData)
corinsDatalongform = melt(corinsData)
ggplot(data = corinsDatalongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Insurance History Attributes')
From this graph its interesting to see that Response is slightly negatively correlated to Insurance_History_2 variable. Insurance_History_3 is negatively correlated to 4, 7, 9. 4 is positively correlated to 7 and 9, 1. 7 is positively correlated 4, 1, 9.
productData = trainingData %>% select(starts_with('Product_Info'), Response, -Product_Info_2)
corProductData = cor(productData)
corProductDatalongform = melt(corProductData)
ggplot(data = corProductDatalongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Product Information Attributes')
After plotting the correlation matrix for all variables and their subtypes, we now move on to fitting models. First we need to handle missing values. For this analysis we drop columns which have missing values.
updatedTrainingData = trainingData[, colSums(is.na(trainingData)) == 0]
updatedTestData = testData[, colSums(is.na(testData)) == 0]
featureNames = names(updatedTrainingData)[2 : (ncol(updatedTrainingData) - 1)]
for(k in featureNames){
if(class(updatedTrainingData[[k]]) == 'character'){
level = unique(c(updatedTrainingData[[k]], updatedTestData[[k]]))
updatedTrainingData[[k]] = as.integer(factor(updatedTrainingData[[k]], levels = level))
updatedTestData[[k]] = as.integer(factor(updatedTestData[[k]], levels = level))
}
}Now that the data is cleaned, lets fit few models
xgbModel = xgboost(data = data.matrix(updatedTrainingData[, featureNames]), label = updatedTrainingData$Response, eta = 0.025, depth = 10, nrounds = 100, objective = 'reg:linear', eval_metric = 'rmse')[1] train-rmse:5.570419
[2] train-rmse:5.450238
[3] train-rmse:5.333451
[4] train-rmse:5.220026
[5] train-rmse:5.109812
[6] train-rmse:5.002814
[7] train-rmse:4.898862
[8] train-rmse:4.797959
[9] train-rmse:4.700017
[10] train-rmse:4.604948
[11] train-rmse:4.512718
[12] train-rmse:4.423228
[13] train-rmse:4.336429
[14] train-rmse:4.252247
[15] train-rmse:4.170590
[16] train-rmse:4.091464
[17] train-rmse:4.014576
[18] train-rmse:3.940206
[19] train-rmse:3.868004
[20] train-rmse:3.798201
[21] train-rmse:3.730433
[22] train-rmse:3.664940
[23] train-rmse:3.601333
[24] train-rmse:3.539867
[25] train-rmse:3.480426
[26] train-rmse:3.422746
[27] train-rmse:3.366975
[28] train-rmse:3.313237
[29] train-rmse:3.261140
[30] train-rmse:3.210736
[31] train-rmse:3.162163
[32] train-rmse:3.115008
[33] train-rmse:3.069538
[34] train-rmse:3.025833
[35] train-rmse:2.983483
[36] train-rmse:2.942677
[37] train-rmse:2.903225
[38] train-rmse:2.865234
[39] train-rmse:2.828328
[40] train-rmse:2.793048
[41] train-rmse:2.758697
[42] train-rmse:2.725726
[43] train-rmse:2.693974
[44] train-rmse:2.663177
[45] train-rmse:2.633579
[46] train-rmse:2.605337
[47] train-rmse:2.577870
[48] train-rmse:2.551438
[49] train-rmse:2.526129
[50] train-rmse:2.501600
[51] train-rmse:2.478041
[52] train-rmse:2.455495
[53] train-rmse:2.433679
[54] train-rmse:2.412680
[55] train-rmse:2.392509
[56] train-rmse:2.373122
[57] train-rmse:2.354504
[58] train-rmse:2.336630
[59] train-rmse:2.319384
[60] train-rmse:2.302925
[61] train-rmse:2.287008
[62] train-rmse:2.271823
[63] train-rmse:2.257182
[64] train-rmse:2.243029
[65] train-rmse:2.229513
[66] train-rmse:2.216529
[67] train-rmse:2.203969
[68] train-rmse:2.191843
[69] train-rmse:2.180212
[70] train-rmse:2.169224
[71] train-rmse:2.158580
[72] train-rmse:2.148142
[73] train-rmse:2.138285
[74] train-rmse:2.128945
[75] train-rmse:2.119676
[76] train-rmse:2.110812
[77] train-rmse:2.102316
[78] train-rmse:2.094156
[79] train-rmse:2.086369
[80] train-rmse:2.078901
[81] train-rmse:2.071867
[82] train-rmse:2.064955
[83] train-rmse:2.058130
[84] train-rmse:2.051683
[85] train-rmse:2.045707
[86] train-rmse:2.039685
[87] train-rmse:2.033930
[88] train-rmse:2.028527
[89] train-rmse:2.023332
[90] train-rmse:2.018152
[91] train-rmse:2.013489
[92] train-rmse:2.008748
[93] train-rmse:2.004300
[94] train-rmse:1.999893
[95] train-rmse:1.995622
[96] train-rmse:1.991551
[97] train-rmse:1.987637
[98] train-rmse:1.983850
[99] train-rmse:1.980297
[100] train-rmse:1.976910 impMatrix = xgb.importance(model = xgbModel)
impMatrixtop10 = impMatrix[c(1:10), ]
xgb.plot.importance(importance_matrix = impMatrixtop10)
impMatrixtop30 = impMatrix[c(1:30), ]
xgb.plot.importance(importance_matrix = impMatrixtop30)
submissionData = data.frame(Id = updatedTestData$Id)
submissionData$Response = round(predict(xgbModel, data.matrix(updatedTestData[, featureNames])))Questions: 1. A refined statement of the model’s purpose and application from project Component 1. The main purpose of model development was to look for parameters that have a strong influence on the final decision associated with an life insurance application.
A refined statement of the data used in the model, as well as transformations and abstractions you performed. In order to acheive this, an Extreme Gradient Boost model was fit on the training dataset. Before XGBoost model was fit on the dataset, the data was scrubbed to discard variables (columns) with NULL values. Further variables of character type (Product_Info_2) was coded with Numeric ID’s based on the assumption that they are categorical. In addition to this during the exploratory data analysis phase several transformation/abstraction technique was used (gather, calculate percent NULL values for each response type etc.)
A refined statement about its validity and generalizability. The parameters selected by the XGBoost model as having a strong influence on the decision of a life insurance application makes sense. We can see Physical attributes (BMI, Age) and Medical History have the strongest influence. In terms of generalizability it seems to make sense that Medical background and Physical attributes of a person have strong influence on life insurance application decision.
A refined graphical depiction of the most revealing and interesting element of the model. When we expand our selection of variable importance to 30 top variables that influence Life insurance decision, it was interesting to see that most of the applicant employment information hardly mattered. Infact, only one of the employment parameters made it to the top 30 list.
---
title: "Prudential Life Insurance Data Analysis"
Author: 'Sumukh Ramesh'
Date: 'November 25th, 2018'
Last Update: ''
output: html_notebook
---

Reading Prudential Life Insurance Data

```{r, echo = FALSE, message = FALSE, warnings = FALSE}
library(tidyverse)
library(ggplot2)
library(plotly)
library(dplyr)
library(skimr)
library(ggthemes)
library(gridExtra)
library(ggforce)
library(car)
library(reshape2)
library(xgboost)
library(caret)

trainingData = read_csv('train.csv')
testData = read_csv('test.csv')
set.seed(10)

```

Quick Snapshot of Data followed by seperating individual variable types
```{r}
names(trainingData)
head(trainingData)
head(testData)

categoricalVarNames = c(paste('Product_Info_', c(1:3,5:7), sep=''), 
                      paste('Employment_Info_', c(2,3,5), sep=""), 
                      paste('InsuredInfo_', 1:7, sep=''), 
                      paste('Insurance_History_', c(1:4,7:9), sep=''),
                      'Family_Hist_1', 
                      paste('Medical_History_', c(2:14, 16:23, 25:31, 33:41), sep=''))

continuousVarNames = c('Product_Info_4', 
                       'Ins_Age', 
                       'Ht', 
                       'Wt', 
                       'BMI', 
                       'Employment_Info_1', 
                       'Employment_Info_4', 
                       'Employment_Info_6', 
                       'Insurance_History_5', 
                       'Family_Hist_2', 
                       'Family_Hist_3', 
                       'Family_Hist_4', 
                       'Family_Hist_5')

discreteVarNames = c('Medical_History_1', 
                     'Medical_History_15', 
                     'Medical_History_24', 
                     'Medical_History_32', 
                     paste('Medical_Keyword_', 1:48, sep=''))

train_categorical = trainingData %>% select(categoricalVarNames)
test_categorical = testData %>% select(categoricalVarNames)

train_continuous = trainingData %>% select(continuousVarNames)
test_continuous = testData %>% select(continuousVarNames)

train_discrete = trainingData %>% select(discreteVarNames)
test_discrete = testData %>% select(discreteVarNames)

```

Let's take a look at the categorical variable type

```{r}
skim(train_categorical)
skim(test_categorical)
```
Let's explore continuous variables
```{r}
skim(train_continuous)
skim(test_continuous)
```

Let's explore discrete variables
```{r}
skim(train_discrete)
skim(test_discrete)
```

From the above skim statements, we see some missing data. What is the precentage of missing data in training and test datasets
```{r}
sum(is.na(trainingData)) / (nrow(trainingData) * ncol(trainingData))
sum(is.na(testData)) / (nrow(testData) * ncol(testData))

apply(trainingData, 2, function(x){
  sum(is.na(x))
})

apply(testData, 2, function(x){
  sum(is.na(x))
})
```


Now let's look at the response variable. The response variable is of nominal type and has levels from 1 to 8.
```{r}
temp1 = ggplot(trainingData, aes(x = Response)) + geom_histogram(fill = 'Blue', alpha=0.5)
ggplotly(temp1, color = ~Response, width = 400, height = 200) %>% layout(title= 'Distribution of Response Variable', plot_bgcolor= 'white', xaxis = list(gridcolor = 'lightgrey', opacity = 0.2), yaxis = list(gridcolor = 'lightgrey', opacity = 0.2), autosize = F, width = 400, height = 200)
```


Based on the above plot we see that Response = 8 is most common and Response = 3 is least common in the dataset. Now let's look at how missing values relate to these responses

```{r}
noofNAperResponseType <- sapply(sort(unique(trainingData$Response)), function(x) {
                            apply(trainingData[trainingData$Response == x, ], 2, function(y) {
              sum(is.na(y)) 
                            }) 
                        })
noofNAperResponseType
round(colSums(noofNAperResponseType) / sum(noofNAperResponseType), digits=2)
```
From this we see that data with Response as 8 has most missing values whereas Response equals 3 has least missing values. Now let's look for Duplicated and Constant Rows

```{r}
cat('Number of duplicated rows in Training dataset = ', nrow(trainingData) - nrow(unique(trainingData)), '\n')
cat('Number of constant rows in Training dataset = ', sum(sapply(trainingData,                                                         function(x){                                                                       length(unique(x)) == 1})), '\n')
                                                        #How to tell a dplyr function to apply it                                                             columnwise
cat('Number of duplicated rows in Test dataset = ', nrow(testData) - nrow(unique(testData)), '\n')
cat('Number of constant rows in Test dataset = ', sum(sapply(testData, function(x){                                                                       length(unique(x)) == 1})), '\n')
```


Now that we have looked at missing values, checked for duplication and constant rows. Let's look at individual data types and visualize the distribution.

Plot densities of continuous features

```{r}
tempDataset = train_continuous %>% select(c(1, 2)) %>% gather()
tempdenPlot = ggplot(data = tempDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots')
ggplotly(tempdenPlot, height= 600, width = 800) %>% 
      layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)

physicalAttDataset = train_continuous %>% select(Ht, Wt, BMI) %>% gather()
tempdenPlot = ggplot(data = physicalAttDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Physical Characteristics')
ggplotly(tempdenPlot, height= 600, width = 800) %>% 
      layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)

employAttDataset = train_continuous %>% select(Employment_Info_1, Employment_Info_6) %>% gather()
tempdenPlot = ggplot(data = employAttDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Employment Info 1 and 6')
ggplotly(tempdenPlot, height= 600, width = 800) %>% 
      layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)

employDataset = train_continuous %>% select(Employment_Info_4) %>% gather()
tempdenPlot = ggplot(data = employDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Employment Info 4')
ggplotly(tempdenPlot, height= 600, width = 800) %>% 
      layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)

familyHistDataset = train_continuous %>% select(starts_with('Family_Hist_')) %>% gather()
tempdenPlot = ggplot(data = familyHistDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots of Family History')
ggplotly(tempdenPlot, height= 600, width = 800) %>% 
      layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 600)

insurHistDataset = train_continuous %>% select(c(9)) %>% gather()
tempdenPlot = ggplot(data = insurHistDataset) + geom_density(mapping = aes(x = value, fill = key, alpha = 0.5)) + labs(title = 'Density Plots')
ggplotly(tempdenPlot, height= 800, width = 1000) %>% 
      layout(plot_bgcolor='transparent', paper_bgcolor= 'transparent', autosize = F, width = 800, height = 800)

```
Plot Boxplots of Continuous Variables
```{r}
tempDataset = train_continuous %>% select(c(1, 2, 9)) %>% gather()
plot_ly(data = tempDataset, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots')
plot_ly(data = physicalAttDataset, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots of Physical Characteristics')
plot_ly(data = familyHistDataset, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots of Family History Attributes')
employInfoData = train_continuous %>% select(starts_with('Employment_Info_')) %>% gather()
plot_ly(data = employInfoData, type = 'box', split = ~key, y = ~value) %>% layout(title = 'Box Plots of Employment Information Attributes')
```
Histograms of Categorical Variables
```{r}
subSelectionVals = c('Product_Info', 'Employment_Info', 'InsuredInfo', 'Insurance_History', 'Family_Hist', 'Medical_History')
for(i in subSelectionVals) {
  tempCatDataset = train_categorical %>% select(starts_with(i))
  tempcolNames = names(tempCatDataset)
   for(j in tempcolNames){
    if(j == 'Product_Info_2' | j == 'Medical_History_10') next
    tempCatPlot = ggplot(data = train_categorical) + geom_histogram(mapping = aes_string(x = j), fill = 'Blue', alpha = 0.5)
    tempCatPlot = ggplotly(tempCatPlot, color = ~j, width = 400, height = 200) %>% layout(title= 'Distribution of Categorical Variable', plot_bgcolor= 'white', xaxis = list(gridcolor = 'lightgrey', opacity = 0.2), yaxis = list(gridcolor = 'lightgrey', opacity = 0.2), autosize = F, width = 400, height = 200)
    print(tempCatPlot)
  }
}
```
From the density plots it looks like Employment_Info_6 has a very similar histogram to the response variable. Its a good indication that this variable maybe a good predictor of the response variable.

Now let's explore to see the relationship and any collinearity that exist between the variables. First lets plot a correlation matrix of all continuous and categorical variables with response

```{r}
corInputVarNames = c(categoricalVarNames, continuousVarNames)
corInputData = trainingData %>% select(corInputVarNames) %>% select(-Product_Info_2)
cor1 = cor(corInputData)
corDatalongform = melt(cor1)
ggplot(data = corDatalongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix excl. Discrete Variables (Medical Keywords)')

```

From this we see some interesting observations:
i. Medica_History_25 and Medical_History_26 show negative correlation.
ii. Variable 7 is strongly correlated with variables 18 and 14.

Now let's look at detail by subsetting the data to identify correlation that exist between bins of input data ie types like Medical History, Product Info etc.

```{r}
medHistData = trainingData %>% select(starts_with('Medical_History'), Response, -c(Medical_History_10, Medical_History_24, Medical_History_32))
cormedHist = cor(medHistData)
corMedHistlongform = melt(cormedHist)
ggplot(data = corMedHistlongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Medical History Variables')

```

From this graph we can see that:
i. Medical_History_25 and Medical_History_26 show negative correlation
ii. Medical_History_36 is postively correlated to Medical_History_15 and negatively correlated to Medical_History_26
iii. Medical_History_7 is storgly correlated with Medical_History_8 and Medical_History_14

```{r}
physicalData = trainingData %>% select(Ins_Age, BMI, Ht, Wt, Response)
corPhyData = cor(physicalData)
corPhyDatalongForm = melt(corPhyData)
ggplot(data = corPhyDatalongForm, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Physical Attributes')

```
From this graph its interesting to see that Response is negatively correlated with Wt/BMI and Ins_Age.

```{r}
insData = trainingData %>% select(starts_with('Insurance_History'), Response)
corinsData = cor(insData)
corinsDatalongform = melt(corinsData)
ggplot(data = corinsDatalongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Insurance History Attributes')
```
From this graph its interesting to see that Response is slightly negatively correlated to Insurance_History_2 variable. Insurance_History_3 is negatively correlated to 4, 7, 9. 4 is positively correlated to 7 and 9, 1. 7 is positively correlated 4, 1, 9.



```{r}
productData = trainingData %>% select(starts_with('Product_Info'), Response, -Product_Info_2)
corProductData = cor(productData)
corProductDatalongform = melt(corProductData)
ggplot(data = corProductDatalongform, mapping = aes(x = Var1, y = Var2, fill = value)) + geom_tile(color = 'white') + scale_fill_gradient2(low = 'blue', high = 'red', mid = 'white', midpoint = 0, limit = c(-1,1)) + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) + ggtitle ('Pearson Correlation Matrix for Product Information Attributes')
```

After plotting the correlation matrix for all variables and their subtypes, we now move on to fitting models. First we need to handle missing values. For this analysis we drop columns which have missing values.

```{r}
updatedTrainingData = trainingData[, colSums(is.na(trainingData)) == 0]
updatedTestData = testData[, colSums(is.na(testData)) == 0]

featureNames = names(updatedTrainingData)[2 : (ncol(updatedTrainingData) - 1)]

for(k in featureNames){
  if(class(updatedTrainingData[[k]]) == 'character'){
   level = unique(c(updatedTrainingData[[k]], updatedTestData[[k]]))
   updatedTrainingData[[k]] = as.integer(factor(updatedTrainingData[[k]], levels = level))
   updatedTestData[[k]] = as.integer(factor(updatedTestData[[k]], levels = level)) 
  }
}
```
Now that the data is cleaned, lets fit few models
```{r}
xgbModel = xgboost(data = data.matrix(updatedTrainingData[, featureNames]), label = updatedTrainingData$Response, eta = 0.025, depth = 10, nrounds = 100, objective = 'reg:linear', eval_metric = 'rmse')
impMatrix = xgb.importance(model = xgbModel)
impMatrixtop10 = impMatrix[c(1:10), ]
xgb.plot.importance(importance_matrix = impMatrixtop10)

impMatrixtop30 = impMatrix[c(1:30), ]
xgb.plot.importance(importance_matrix = impMatrixtop30)
```

```{r}
submissionData = data.frame(Id = updatedTestData$Id)
submissionData$Response = round(predict(xgbModel, data.matrix(updatedTestData[, featureNames])))
```
Questions:
1. A refined statement of the model's purpose and application from project Component 1.
  The main purpose of model development was to look for parameters that have a strong influence on the final decision associated with an life insurance application.
  
2. A refined statement of the data used in the model, as well as transformations and abstractions you performed.
  In order to acheive this, an Extreme Gradient Boost model was fit on the training dataset. Before XGBoost model was fit on the dataset, the data was scrubbed to discard variables (columns) with NULL values. Further variables of character type (Product_Info_2) was coded with Numeric ID's based on the assumption that they are categorical. In addition to this during the exploratory data analysis phase several transformation/abstraction technique was used (gather, calculate percent NULL values for each response type etc.)
  
3. A refined statement about its validity and generalizability.
  The parameters selected by the XGBoost model as having a strong influence on the decision of a life insurance application makes sense. We can see Physical attributes (BMI, Age) and Medical History have the strongest influence. In terms of generalizability it seems to make sense that Medical background and Physical attributes of a person have strong influence on life insurance application decision.
  
4. A refined graphical depiction of the most revealing and interesting element of the model.
  When we expand our selection of variable importance to 30 top variables that influence Life insurance decision, it was interesting to see that most of the applicant employment information hardly mattered. Infact, only one of the employment parameters made it to the top 30 list.